Ni/Ni3C Core-Shell Nanochains and Its Magnetic Properties: One-Step Synthesis at low temperature

One-dimensional Ni/Ni3C core-shell nanoball chains with an average diameter by around 30 nm were synthesized by means of a mild chemical solution method using a soft template of trioctylphosphineoxide (TOPO). It was revealed that the uniform Ni nanochains were capped with Ni3C thin shells by about 1 to 4 nm in thickness and each Ni core consists of polygrains. The coercivity of the core-shell nanochains is much enhanced (600 Oe at 5 K) and comparable with single Ni nanowires due to the one-dimensional shape anisotropy. Deriving from the distinctive structure of Ni core and Ni3C shell, this architecture may possess a possible bi-functionality. This unique architecture is also useful for the study on the magnetization reversal mechanism of one-dimensional magnetic nanostructure.Comment: 17 pages, 6 figur

Effects of Exogenous Melatonin on Body Mass Regulation and Hormone Concentrations in Eothenomys miletus

By regulating the pineal hormone, photoperiods affect many physiological characteristics in small mammals. Thus, melatonin might take part in the thermoregulation of seasonal variations in small mammals. This study determined the influence of melatonin treatment on thermogenic pattern, we measured body mass, thermogenic activities and hormone concentrations of Eothenomys miletus were given exogenous melatonin (MLT) for 28 days. The results shown that body mass was reduced significantly, whereas resting metabolic rate (RMR) and nonshivering thermogenesis (NST) increased at 28 days in MLT group compared to control group as well as the oxidative capacities of mitochondria in liver and brown adipose tissue (BAT) were enhanced; the contents of total and mitochodrial protein increased markedly. Melatonin treatment significantly increased the State 3, State 4 respiration of liver mitochondria, and the activity of cytochrome C oxidase (COX) in liver; but the α-glerocephasphate oxidase (α-PGO) capacity showed no differences during the acclimation in liver. Furthermore, the State 4 respiration, the activities of COX and α-PGO in BAT increased, respectively. The activity of thyroxin 5’-deiodinase ( T45’-DII) in BAT increased remarkably. The serum content of thyroxine (T 4) decreased, and that of tri-iodothyronine (T 3) increased. Moreover, serum leptin levels showed no significant differences in MLT group compared to control group. Together, these data indicate that melatonin enhances thermogenic capacity in E. miletus. Our results suggested that melatonin is potentially involved in the regulation of body mass, adaptive thermogenic capacity and hormone concentrations in E. miletus

SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model

Generic image inpainting aims to complete a corrupted image by borrowing surrounding information, which barely generates novel content. By contrast, multi-modal inpainting provides more flexible and useful controls on the inpainted content, \eg, a text prompt can be used to describe an object with richer attributes, and a mask can be used to constrain the shape of the inpainted object rather than being only considered as a missing area. We propose a new diffusion-based model named SmartBrush for completing a missing region with an object using both text and shape-guidance. While previous work such as DALLE-2 and Stable Diffusion can do text-guided inapinting they do not support shape guidance and tend to modify background texture surrounding the generated object. Our model incorporates both text and shape guidance with precision control. To preserve the background better, we propose a novel training and sampling strategy by augmenting the diffusion U-net with object-mask prediction. Lastly, we introduce a multi-task training strategy by jointly training inpainting with text-to-image generation to leverage more training data. We conduct extensive experiments showing that our model outperforms all baselines in terms of visual quality, mask controllability, and background preservation

UrbanFM: Inferring Fine-Grained Urban Flows

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a method entitled UrbanFM based on deep neural networks. Our model consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs by using a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influences of different external factors. Extensive experiments on two real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness and efficiency of our method compared to seven baselines, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow inference problem

Molecular Beam Epitaxy Growth of Superconducting LiFeAs Film on SrTiO3(001) Substrate

The stoichiometric "111" iron-based superconductor, LiFeAs, has attacted great research interest in recent years. For the first time, we have successfully grown LiFeAs thin film by molecular beam epitaxy (MBE) on SrTiO3(001) substrate, and studied the interfacial growth behavior by reflection high energy electron diffraction (RHEED) and low-temperature scanning tunneling microscope (LT-STM). The effects of substrate temperature and Li/Fe flux ratio were investigated. Uniform LiFeAs film as thin as 3 quintuple-layer (QL) is formed. Superconducting gap appears in LiFeAs films thicker than 4 QL at 4.7 K. When the film is thicker than 13 QL, the superconducting gap determined by the distance between coherence peaks is about 7 meV, close to the value of bulk material. The ex situ transport measurement of thick LiFeAs film shows a sharp superconducting transition around 16 K. The upper critical field, Hc2(0)=13.0 T, is estimated from the temperature dependent magnetoresistance. The precise thickness and quality control of LiFeAs film paves the road of growing similar ultrathin iron arsenide films.Comment: 7 pages, 6 figure

Variations of Endocrine Hormones Concentrations in Tupaia belangeri under Simulated Seasonal Acclimatized: Role of Leptin Sensitivity

Seasonal variations in endocrine hormones concentrations are important for the survival of small mammals during acclimatization. In order to understand the role of leptin sensitivity on other endocrine hormones concentrations, we examined body mass, serum leptin level, serum insulin, tri-iodothyronine (T 3), thyroxine (T4) and thyroid stimulating hormone (TSH) concentrations in Tupaia belangeri under seasonal acclimatized (The simulated temperature and photoperiod in winter: 5°C and SD, 8h:16h Light:Dark; the simulated temperature and photoperiod in summer: 30°C and SD, 16h:8h Light:Dark) for 4 weeks. The results showed that body mass, serum leptin level, serum T3, T4 concentrations and T3/T4 showed significant variation, but serum insulin and TSH concentrations showed no variations between treatment group. There were positive correlation between serum leptin level and insulin, T4 concentrations, and were negative correlation between serum leptin level and body mass, T3 concentrations. However, no correlation was found between serum TSH concentrations and serum leptin level. The present results suggested T. belangeri overcome winter thermogenesis challenges by adjusting body mass and endocrine hormones concentrations. Furthermore, leptin may play an potential role in their body mass regulation in T. belangeri

Explainable Recommender with Geometric Information Bottleneck

Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanation generation or leverage the attention mechanism to extract important text spans from reviews as explanations. The extracted rationales are often confined to an individual review and may fail to identify the implicit features beyond the review text. To avoid the expensive human annotation process and to generate explanations beyond individual reviews, we propose to incorporate a geometric prior learnt from user-item interactions into a variational network which infers latent factors from user-item reviews. The latent factors from an individual user-item pair can be used for both recommendation and explanation generation, which naturally inherit the global characteristics encoded in the prior knowledge. Experimental results on three e-commerce datasets show that our model significantly improves the interpretability of a variational recommender using the Wasserstein distance while achieving performance comparable to existing content-based recommender systems in terms of recommendation behaviours